The rapid development of the Internet of Things (IoT) has transformed business and customer services in many aspects of modern life with ubiquitous sensing and computing capabilities of mobile devices such as smartphones, laptops, and tablets [1]}. Aiming at obtaining the insights of data generated from these mobile devices, artificial intelligent (AI) disciplines such as machine learning (ML) have been widely exploited to train data models for enabling intelligent applications [2]}.
To accommodate the unpredictable demands of computation and storage for AI services, mobile edge computing (MEC) has been proposed for data training with low latency, by exploiting resources at the network edge near the data sources [3]}.
Given the increasing volume of IoT data and the growing concern of data privacy in next-generation wireless networks, implementing centralized AI training at the central server may be no longer suitable. Federated learning (FL) has recently emerged as a
distributed
AI approach, by coordinating multiple devices to perform AI training without sharing raw data for privacy enhancement and network resource (e.g., bandwidth) savings. For example, in the context of MEC networks, distributed mobile devices can collaborate with an MEC server to perform an FL process [4]}, by training data locally and exchanging learning parameters with the aggregation server in a number of communication rounds until the global training is complete. In this way, users do not need to offload their raw data to the server, which is able to enhance user privacy and save communication resources required for data transmission.
